# Connectomics ## Microsim (three.js) <div class="microsim-player"> <iframe src="https://wikitube-3d-microsims.netlify.app/Connectomics.html" width="100%" height="620" frameborder="0" loading="lazy" sandbox="allow-scripts allow-same-origin"></iframe> </div> *Part of the GENOMICS hub's [[Neuroscience]] track: where DNA sequencing reads a genome letter by letter, connectomics reads the brain's [[Neural_circuit|neural circuitry]] connection by connection — mapping the wiring that emerges through nervous-system development.* > Connectomics is the science of building and studying **connectomes** — comprehensive maps of the connections in a nervous system, from single synapses to whole-brain pathways. Because a brain is essentially a network of neurons joined by synapses, connectomes are treated as **graphs**: cells (or regions) become nodes and connections become edges. This microsim generates such a network and lets you reshape its topology and prune its weakest links. By doing so, you can watch a brain-like graph slide between orderly, clustered wiring and shortcut-laden random wiring — the very trade-off that real brains must solve. ## About this microsim The simulation renders a network of nodes and connections that you steer with two sliders and a row of buttons. **Rewiring probability p (topology)** ranges from 0 to 1 and controls how much of the network's regular local wiring is randomly rewired into long-range shortcuts. **Connection-strength threshold** (0 to 1) hides every edge weaker than the cut-off, mimicking how analysts binarise a weighted connectome. **Play** animates the layout continuously and **Step** advances a single frame; **Reset seed** resets the random seed that generates the network, while **Regenerate network** rebuilds the graph at the current settings. You orbit the view with the mouse, and **Reset camera** returns it to the default angle. There are no other hidden parameters. ## Related microsims - [[Neural_circuit]] — the functional units whose connections a connectome catalogues - Neuroplasticity — how real wiring is added, pruned, and re-weighted over time - Development of the nervous system — the processes that build a connectome in the first place - Action potential — the signal that travels along the edges mapped here - Neural decoding — reading behaviour and perception out of network activity - Electroencephalography — a macroscale window on the dynamics these graphs support ## Links (Wikipedia order) <!-- injected from _registry/childlinks/Connectomics.json (2026-07-30T02:09:12Z) --> `Affective_neuroscience` · `Apical_dendrite` · `Ashlee_Vance` · `Basal_ganglia` · `Basic_research` · `Behavioral_epigenetics` · `Behavioral_neurology` · `Behavioral_neuroscience` · `Behavioural_genetics` · `Betweenness_centrality` · `Biochip` · [[Bioinformatics]] · `Brain-reading` · `Brain_mapping` · `Brainstem` · `Brain–computer_interface` · `Budapest_Reference_Connectome` · `Caenorhabditis_elegans` · `Calcium_imaging` · `Cellular_neuroscience` · `Central_nervous_system` · [[Centrality]] · `Cerebral_hemisphere` · `Cheminformatics` · `Chemogenomics` · `Chromosome_conformation_capture` · `Chronobiology` · `Clinical_neurophysiology` · `Clinical_neuroscience` · `Cognitive_genomics` · `Cognitive_neuroscience` · `Comparative_genomics` · `Computational_genomics` · `Computational_neuroscience` · `Connectogram` · `Consciousness` · `Consumer_neuroscience` · `Correlative_light-electron_microscopy` · `Cultural_neuroscience` · `Cumulative_distribution_function` · `Cuneus` · `Current_Biology` · `Cut_(graph_theory)` · `DNA_Data_Bank_of_Japan` · `Danionella` · [[Detection_theory]] · `Development_of_the_nervous_system` · `Diffusion-weighted_magnetic_resonance_imaging` · `Drosophila` · `Drosophila_connectome` · `Drosophila_melanogaster` · `Dynamic_functional_connectivity` · `Educational_neuroscience` · `Eigengap` · `Electrospray_ionization` · `Enteric_nervous_system` · `Epigenomics` · `Epitranscriptome` · `European_Molecular_Biology_Laboratory` · `Evolutionary_neuroscience` · `Expander_graph` · `Expansion_microscopy` · `Extracellular_space` · `Fractional_anisotropy` · `Functional_genomics` · `Functional_magnetic_resonance_imaging` · `Gene` · `Genetic_code` · `Genome` · `Genome_project` · [[Genomics]] · `Global_neurosurgery` · `Glycomics` · `Google` · `Graph_(discrete_mathematics)` · [[Graph_theory]] · `Harvard_Medical_School` · `History_of_microscale_connectomics` · `History_of_neuroscience` · `Human_Connectome_Project` · `Human_Epigenome_Project` · `Human_Genome_Project` · `Human_Metabolome_Database` · `Human_Microbiome_Project` · `Image_segmentation` · `Imaging_genetics` · `Immunomics` · `Indiana_University` · `Inferior_parietal_lobule` · `Instar` · `Insular_cortex` · `Integrative_neuroscience` · `Intraoperative_neurophysiological_monitoring` · `Karel_Svoboda_(scientist)` · `Large-scale_brain_network` · `Larva` · `Lausanne_University_Hospital` · `Lipidomics` · `List_of_functional_connectivity_software` · `List_of_omics_topics_in_biology` · `MRC_Laboratory_of_Molecular_Biology` · `Metabolomics` · `Metagenomics` · `Microbiome` · `Mirror_neuron` · `Molecular_cellular_cognition` · `Molecular_neuroscience` · `Motor_control` · `Mouse` · `Multiomics` · `Multistage_interconnection_networks` · `Nanotomography` · `National_Institutes_of_Health` · `Nematode` · `Nerve_tract` · [[Nervous_system]] · [[Network_science]] · `Neural_basis_of_self` · [[Neural_circuit]] · `Neural_circuit_reconstruction` · `Neural_decoding` · `Neural_engineering` · [[Neural_network_(machine_learning)]] · `Neurite` · `Neuro-oncology` · `Neuro-ophthalmology` · `Neuroanatomy` · `Neuroanthropology` · `Neurobranding` · `Neurocardiology` · `Neurochemistry` · `Neurochip` · `Neurocinema` · `Neurocriminology` · `Neurodegenerative_disease` · `Neurodevelopmental_disorder` · `Neurodiversity` · 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`Oncogenomics` · `Open_access` · `Organism` · `Outline_of_neuroscience` · `Paleoneurobiology` · `Paleopolyploidy` · `Paracentral_lobule` · `Personal_genomics` · `Pharmacogenomics` · `Phineas_Gage` · `Platynereis_dumerilii` · `Population_genomics` · `Posterior_cingulate_cortex` · `Precuneus` · `Primary_somatosensory_cortex` · `Proteomics` · `Psychiatry` · `Region_of_interest` · `Resting_state_fMRI` · `STED_microscopy` · `Sebastian_Seung` · `Sensory_neuroscience` · `Shrub` · [[Small-world_network]] · `Social_cognitive_neuroscience` · `Social_neuroscience` · `Sociogenomics` · `Structural_biology` · `Structural_genomics` · `Super-resolution_microscopy` · `Superior_frontal_gyrus` · `Superior_parietal_lobule` · `Superior_temporal_sulcus` · `Synapse` · `Synaptic_plasticity` · `Synchrotron_light_source` · [[Systems_biology]] · [[Systems_neuroscience]] · `TED_(conference)` · `Toxicogenomics` · `Transcranial_magnetic_stimulation` · `Transcriptomics_technologies` · `Traumatic_brain_injury` · `Ventral_nerve_cord` · `Vertex_cover` · `Webknossos` · `Wellcome_Sanger_Institute` · `Western_barn_owl` · `Zebrafish` ## Overview The term *connectome* was coined in 2005 by analogy with the *genome*: just as a genome is the full complement of genes, a connectome is the full set of neural connections. Connectomics spans scales — **macroscale** maps of brain regions linked by white-matter tracts (measured with diffusion-MRI tractography, as in the Human Connectome Project), **mesoscale** maps of cell populations, and **microscale** maps of individual neurons and synapses reconstructed from electron microscopy. The first complete connectome, of the nematode *Caenorhabditis elegans* (302 neurons, roughly 7,000 connections), was published in 1986. Reconstructing the human brain's ~86 billion neurons and ~$10^{14}$ synapses remains far off, but the entire adult fruit-fly brain (~140,000 neurons) was mapped in 2024. The guiding premise is that structure constrains function: wiring shapes behaviour, development, and disease. ## The mechanism A connectome is formalised as a graph $G = (V, E)$, where vertices $V$ are neurons or regions and edges $E$ are connections, often weighted by synapse count or tract strength. Analysts describe it with metrics such as the **clustering coefficient** $C$ (how tightly a node's neighbours interconnect) and the **characteristic path length** $L$ (average hops between nodes). Real brains are **small-world networks** — high $C$ *and* low $L$ — a regime captured by the Watts–Strogatz model that this microsim implements: start from a regular ring lattice and rewire each edge with probability $p$. | $p$ | Regime | Clustering $C$ | Path length $L$ | |-----|--------|----------------|-----------------| | 0 | Regular lattice | High | Long | | ~0.01–0.1 | Small-world | High | Short | | 1 | Random graph | Low | Short | Brains sit in that middle band, balancing **segregation** (local, clustered processing) against **integration** (fast global communication) at low wiring cost. The strength threshold reproduces a routine preprocessing step: weak, noisy edges are dropped before metrics are computed, which can fragment the network if set too high. ## Controls -> what each maps to | Control | Maps to | Range / values | Meaning | |---------|---------|----------------|---------| | Rewiring probability p (topology) | Watts–Strogatz rewiring parameter | 0–1 | Fraction of edges rewired into shortcuts; 0 = lattice, 1 = random | | Connection-strength threshold | Edge-weight cut-off | 0–1 | Hides connections weaker than the value, as when binarising a connectome | | Play | Animation toggle | button | Runs the layout continuously | | Step | Single advance | button | Moves the simulation one frame | | Reset seed | RNG reinitialisation | button | Resets the random seed that builds the network | | Regenerate network | Rebuild graph | button | Draws a new network at the current settings | | Reset camera | View reset | button | Returns the orbit view to its default angle | ## Learning objective Understand how a nervous system can be represented as a graph and how a single rewiring parameter moves it between regular, small-world, and random topologies. ## Limits and connections The sim is a topological toy model, not a reconstructed connectome: nodes carry no cell-type identity, edges no directionality or neurotransmitter, and no real electron-microscopy or MRI data are shown. Still, it captures the core insight that wiring statistics — not just individual cells — shape how brains compute, adapt, and, in plasticity, rewire. ## Poster & source <div class="microsim-fallback"> <!-- poster image pending backfill --> <p><em>Live microsim · <a href="https://wikitube-3d-microsims.netlify.app/Connectomics.html">open full</a> · source: Microsims for Dissemination/GENOMICS_ThreeJS_Microsims/Connectomics.html</em></p> </div> <!-- CRAFT-LINK:START g12 --> *Built to the [[WT!Three_js_Microsim_Master_Class|three.js Master Class]].* <!-- CRAFT-LINK:END --> ## Wikipedia : Wikitube **Strict pair:** [Wikipedia](https://en.wikipedia.org/wiki/Connectomics) : [Wikitube](https://en.wikitube.io/wiki/Connectomics) ## Previous hub tags Tree parent: [[Graph_theory]]. Legacy hubs: `GENOMICS`. --- *Sources: 1 legacy note. Minted wave 1, 2026-07-30 (v1.6 order).*